With the emphasis on early detection of cancer, more and more people are taking part in early screening programs, such as mammography screening and in some parts of the world ultrasound screening for breast cancer. Some recent studies suggest that diagnostic breast ultrasonography may successfully help distinguish many benign from malignant solid lesions or nodules. For example, in “Solid breast nodules: use of sonography to distinguish between benign and malignant lesions,” by Stavros, A. T., et al., Radiology 196:123-134, 1995 (“Stavros”), it was suggested that sonography may be used to accurately classify some solid lesions as benign, allowing imaging follow-up rather than biopsy. Stavros provides a general method of reviewing lesions by detecting and evaluating characteristics of sonographic images corresponding to a set of pre-defined characteristics and their description (“Stavros characteristics”). Such local characteristics may include local spiculation, local branch pattern, local duct extension and local micro-lobulation, among others.
In general, successful early detection of abnormalities and diagnosis of cancer requires a radiologist to successfully and correctly identify and evaluate characteristics of masses seen in individual medical images in order to distinguish benign from malignant solid nodules. Medical images are not limited to those obtained from mammography or ultrasound screenings namely X-ray images (or digitized X-ray images) or sonographic images, but may include medical images obtained from any suitable medical scanning device utilizing any underlying image acquisition technology. Some examples of such medical images include sonographic images, Doppler images, spectral Doppler images, X-ray images, computed tomography (CT) images, positron emission tomography (PET) images, PET-CT images and magnetic resonance imaging (MRI) images.
The experience and expertise of an examining radiologist plays an important role in correctly identifying the characteristics so that a well-informed diagnosis may be established. Computer-aided detection has become an increasingly essential problem-solving tool in detecting and diagnosing cancer and other diseases. Modem technology has been advancing in many different ways to aid a radiologist to automatically identify and evaluate a battery of characteristics of masses seen in medical images. For example, technology has been developed to aid a radiologist to automatically identify and evaluate sonographic characteristics, to distinguish benign features in medical images from sonographic findings of malignancy, and to combine individual benign findings and malignant findings to classify a nodule as either benign or malignant in order to make a diagnosis. It is also known to automatically detect and mark candidate lesion or potential abnormalities within the image and thereby assist radiologists in the interpretation of medical images. General availability or accessibility of digitized medical imaging further facilitates the computerized image processing and computer-aided detection.
However, while computerized pattern recognition has seen tremendous advances in the past decade or so, sometimes, a computer application may still have difficulty in identifying most or all abnormalities. It is desirable not to miss a malignant lesion in the early stage of disease. As a radiologist may not place too high a confidence in results of automated detection, biopsy may be ordered, which sometimes turn out to be unnecessary. Further, even if successful detection of all relevant characteristics in a medical image were possible, automated diagnosis may not always provide a correct diagnosis due to, for example, inadequacy or lack of sophistication of models underlying a diagnosis engine.
The foregoing creates challenges and constraints for all CAD systems for extracting, i.e., identifying characteristics and medical features in medical images and suggesting diagnosis based on characteristics automatically detected in the medical image. There is therefore a need for a CAD system and method as compared to the existing art. It is an object of the present invention to mitigate or obviate at least one of the above mentioned disadvantages.